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GPMS: A Genetic Programming Based Approach to Multiple Alignment of Liquid Chromatography-Mass Spectrometry Data

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Applications of Evolutionary Computation (EvoApplications 2014)

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Abstract

Alignment of samples from Liquid chromatography-mass spectrometry (LC-MS) measurements has a significant role in the detection of biomarkers and in metabolomic studies.The machine drift causes differences between LC-MS measurements, and an accurate alignment of the shifts introduced to the same peptide or metabolite is needed. In this paper, we propose the use of genetic programming (GP) for multiple alignment of LC-MS data. The proposed approach consists of two main phases. The first phase is the peak matching where the peaks from different LC-MS maps (peak lists) are matched to allow the calculation of the retention time deviation. The second phase is to use GP for multiple alignment of the peak lists with respect to a reference. In this paper, GP is designed to perform multiple-output regression by using a special node in the tree which divides the output of the tree into multiple outputs. Finally, the peaks that show the maximum correlation after dewarping the retention times are selected to form a consensus aligned map.The proposed approach is tested on one proteomics and two metabolomics LC-MS datasets with different number of samples. The method is compared to several benchmark methods and the results show that the proposed approach outperforms these methods in three fractions of the protoemics dataset and the metabolomics dataset with a larger number of maps. Moreover, the results on the rest of the datasets are highly competitive with the other methods.

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References

  1. Lange, E., Gröpl, C., Schulz-Trieglaff, O., Leinenbach, A., Huber, C.G., Reinert, K.: A geometric approach for the alignment of liquid chromatography-mass spectrometry data. Bioinformatics 23(13), 273–281 (2007)

    Article  Google Scholar 

  2. Vandenbogaert, M., Li-Thiao-Te, S., Kaltenbach, H., Zhang, R., Aittokallio, T., Schwikowski, B.: Alignment of LC-MS images, with applications to biomarker discovery and protein identification. Proteomics 8(4), 650–672 (2008)

    Article  Google Scholar 

  3. Lange, E., Tautenhahn, R., Neumann, S., Gropl, C.: Critical assessment of alignment procedures for LC-MS proteomics and metabolomics measurements. BMC Bioinformatics 9(1), 375–394 (2008)

    Article  Google Scholar 

  4. Heidi Vhmaa, Ville R. Koskinen, W.H.: PolyAlign: A versatile LC-MS data alignment tool for landmark-selected and automated use. International Journal of Proteomics, pp. 1–10 (2011)

    Google Scholar 

  5. Listgarten, J., Neal, R., Roweis, S., Wong, P., Emili, A.: Difference detection in LC-MS data for protein biomarker discovery. Bioinformatics 23(2), 198–204 (2007)

    Article  Google Scholar 

  6. Pluskal, T., Castillo, S., Villar-Briones, A., Oresic, M.: MZmine 2: Modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data. BMC Bioinformatics 11, 395 (2010)

    Article  Google Scholar 

  7. Palmblad, M., Mills, D.J., Bindschedler, L.V., Cramer, R.: Chromatographic Alignment of LC-MS and LC-MS/MS Datasets by Genetic Algorithm Feature Extraction. Journal of the American Society for Mass Spectrometry 18(10), 1835–1843 (2007)

    Article  Google Scholar 

  8. Poli, R., Langdon, W.B., McPhee, N.F.: A field guide to genetic programming. Lulu Enterprises, UK Ltd. (2008)

    Google Scholar 

  9. Ahalpara, D.P.: Improved forecasting of time series data of real system using genetic programming. In: Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation, GECCO 2010, pp. 977–978. ACM, New York (2010)

    Google Scholar 

  10. Smart, W.D., Zhang, M.: Probability based genetic programming for multiclass object classification. In: Proceedings of the 8th Pacific Rim International Conference on Artificial Intelligence, pp. 251–261 (2004)

    Google Scholar 

  11. Rodríguez-Vázquez, K., Oliver-Morales, C.: Multi-branches Genetic Programming as a Tool for Function Approximation. In: Deb, K., Tari, Z. (eds.) GECCO 2004. LNCS, vol. 3103, pp. 719–721. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  12. Zhang, Y., Zhang, M.: A multiple-output program tree structure in genetic programming. In: Proceedings of The Second Asian-Pacific Workshop on Genetic Programming, pp. 1–12 (2004)

    Google Scholar 

  13. Defoin Platel, M., Vérel, S., Clergue, M., Chami, M.: Density Estimation with Genetic Programming for Inverse Problem Solving. In: Ebner, M., O’Neill, M., Ekárt, A., Vanneschi, L., Esparcia-Alcázar, A.I. (eds.) EuroGP 2007. LNCS, vol. 4445, pp. 45–54. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  14. Prince, J., Carlson, M., Lu, R., Marcotte, E.: The need for a public proteomics repository. Nat. Biotechnol. 22, 471–472 (2004)

    Article  Google Scholar 

  15. Kohlbacher, O., Reinert, K., Gropl, C., Lange, E., Pfeifer, N., Schulz-Trieglaff, O., Sturm, M.: TOPP-the OpenMS proteomics pipeline. Bioinformatics 23(2), 191–197 (2007)

    Article  Google Scholar 

  16. Smith, C., Want, E., O’Maille, G., Abagyan, R., Siuzdak, G.: XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal. Chem. 78(3), 779–787 (2006)

    Article  Google Scholar 

  17. White, D.R.: Software review: the ECJ toolkit, 65–67 (2012)

    Google Scholar 

  18. Bellew, M., Coram, M., Fitzgibbon, M., Igra, M., Randolph, T., Wang, P., May, D., Eng, J., Fang, R., Lin, C., Chen, J., Goodlett, D., Whiteaker, J., Paulovich, A., McIntosh, M.: A suite of algorithms for the comprehensive analysis of complex protein mixtures using high-resolution LC-MS. Bioinformatics 22(15), 1902–1909 (2006)

    Article  Google Scholar 

  19. Katajamaa, M., Miettinen, J., Oresic, M.: MZmine: Toolbox for processing and visualization of mass spectrometry based molecular profile data. Bioinformatics 22, 634–636 (2006)

    Article  Google Scholar 

  20. Li, X., Yi, E., Kemp, C., Zhang, H., Aebersold, R.: A software suite for the generation and comparison of peptide arrays from sets of data collected by Liquid Chromatography-Mass Spectrometry. Molecular & Cellular Proteomics: MCP 4(9), 1328–1340 (2005)

    Article  Google Scholar 

  21. Zhang, X., Asara, J., Adamec, J., Ouzzani, M., Elmagarmid, A.: Data pre-processing in liquid chromatography/mass spectrometry-based proteomics. Bioinformatics 21(21), 4054–4059 (2005)

    Article  Google Scholar 

  22. Voss, B., Hanselmann, M., Renard, B., Lindner, M., Kthe, U., Kirchner, M., Hamprecht, F.: Sima: simultaneous multiple alignment of lc/ms peak lists. Bioinformatics 27(7), 987–993 (2011)

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Engineering and Computer Science, Wellington, New Zealand

    Soha Ahmed & Mengjie Zhang

  2. Victoria University of Wellington, 600, Wellington, 6140, New Zealand

    Lifeng Peng

Authors
  1. Soha Ahmed
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  2. Mengjie Zhang
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  3. Lifeng Peng
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Corresponding author

Correspondence to Soha Ahmed .

Editor information

Editors and Affiliations

  1. Depto. Estadística e Investigación, Universidad Politécnica de Valencia, Valencia, Spain

    Anna I. Esparcia-Alcázar

  2. University of Granada, Granada, Spain

    Antonio M. Mora

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Ahmed, S., Zhang, M., Peng, L. (2014). GPMS: A Genetic Programming Based Approach to Multiple Alignment of Liquid Chromatography-Mass Spectrometry Data. In: Esparcia-Alcázar, A., Mora, A. (eds) Applications of Evolutionary Computation. EvoApplications 2014. Lecture Notes in Computer Science(), vol 8602. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45523-4_74

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  • DOI: https://doi.org/10.1007/978-3-662-45523-4_74

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-45522-7

  • Online ISBN: 978-3-662-45523-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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